In [7]:
sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo', 'pct_counts_hb'],
jitter=0.4, groupby = 'sample', rotation= 45)
Some individual studies were analysed to ascertain the presence of batch effects within a single study. Two high performing batch correction methods were also tested on these datasets.
import os
import numpy as np
import pandas as pd
import scanpy as sc
import scanpy.external as sce
import anndata as ad
from anndata import AnnData
import matplotlib.pyplot as plt
from scipy import stats
import scvi
from rich import print
from scib_metrics.benchmark import Benchmarker
from scvi.model.utils import mde
from scvi_colab import install
import scanorama
sc.settings.verbosity = 3
sc.logging.print_header()
sc.settings.set_figure_params(dpi=80, facecolor='white')
if not os.path.exists("outs"):
os.makedirs("outs")
if not os.path.exists("plots"):
os.makedirs("plots")
combined_file = 'outs/zhang_filtered.h5ad'
corrected_file = 'outs/zhang_batch_correct.h5ad'
Global seed set to 0 /Users/jwhittle/opt/anaconda3/envs/scvi-env/lib/python3.9/site-packages/flax/struct.py:132: FutureWarning: jax.tree_util.register_keypaths is deprecated, and will be removed in a future release. Please use `register_pytree_with_keys()` instead. jax.tree_util.register_keypaths(data_clz, keypaths) /Users/jwhittle/opt/anaconda3/envs/scvi-env/lib/python3.9/site-packages/flax/struct.py:132: FutureWarning: jax.tree_util.register_keypaths is deprecated, and will be removed in a future release. Please use `register_pytree_with_keys()` instead. jax.tree_util.register_keypaths(data_clz, keypaths)
scanpy==1.9.3 anndata==0.8.0 umap==0.5.3 numpy==1.23.5 scipy==1.9.1 pandas==1.5.3 scikit-learn==1.2.2 statsmodels==0.14.0 python-igraph==0.10.4 louvain==0.8.0 pynndescent==0.5.8
import rpy2
%load_ext rpy2.ipython
from rpy2.robjects.packages import importr
# import R tools
base = importr('base')
utils = importr('utils')
%%R
suppressMessages(library(kBET))
# Read in data files - with cells already filtered
amlP105 = sc.read_h5ad("../amlP105/outs/AMLP105_cell_filtered.h5ad")
amlP106 = sc.read_h5ad("../amlP106/outs/AMLP106_cell_filtered.h5ad")
amlP108 = sc.read_h5ad("../amlP108/outs/AMLP108_cell_filtered.h5ad")
amlP114 = sc.read_h5ad("../amlP114/outs/AMLP114_cell_filtered.h5ad")
amlP115 = sc.read_h5ad("../amlP115/outs/AMLP115_cell_filtered.h5ad")
amlP116 = sc.read_h5ad("../amlP116/outs/AMLP116_cell_filtered.h5ad")
amlP117 = sc.read_h5ad("../amlP117/outs/AMLP117_cell_filtered.h5ad")
amlP118 = sc.read_h5ad("../amlP118/outs/AMLP118_cell_filtered.h5ad")
amlP119 = sc.read_h5ad("../amlP119/outs/AMLP119_cell_filtered.h5ad")
amlP120 = sc.read_h5ad("../amlP120/outs/AMlP120_cell_filtered.h5ad")
amlP122 = sc.read_h5ad("../amlP122/outs/AMLP122_cell_filtered.h5ad")
amlP123 = sc.read_h5ad("../amlP123/outs/AMLP123_cell_filtered.h5ad")
amlP124 = sc.read_h5ad("../amlP124/outs/AMLP124_cell_filtered.h5ad")
data = [amlP105,amlP106,amlP108,amlP114,amlP115,amlP116,amlP117,amlP118,amlP119,
amlP120,amlP122,amlP123,amlP124]
# Concetenate files
adata = ad.concat(data, merge="same")
# Delete individual datasets to save space
del(amlP105,amlP106,amlP108,amlP114,amlP115,amlP116,amlP117,amlP118,amlP119,
amlP120,amlP122,amlP123,amlP124)
# Annotate mitochondrial/ribosomal/hb genes
adata.var['mt'] = adata.var_names.str.startswith('MT-')
adata.var['ribo'] = adata.var_names.str.startswith(("RPS","RPL"))
adata.var['hb'] = adata.var_names.str.contains(("^HB[^(P)]"))
# Run QC function
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt','ribo', 'hb'], percent_top=None, log1p=False, inplace=True)
# Add the total counts per cell
adata.obs['n_counts'] = adata.X.sum(axis=1).A1
sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo', 'pct_counts_hb'],
jitter=0.4, groupby = 'sample', rotation= 45)
sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt')
sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts', color='pct_counts_mt')
sc.pp.filter_genes(adata, min_cells=10)
print(adata.n_obs, adata.n_vars)
filtered out 11582 genes that are detected in less than 10 cells
75648 21956
sc.pl.highest_expr_genes(adata, n_top=20)
normalizing counts per cell
finished (0:00:01)
# MALAT1 Filtering
malat1 = adata.var_names.str.startswith('MALAT1')
keep = np.invert(malat1)
adata = adata[:,keep]
print(adata.n_obs, adata.n_vars)
75648 21955
sc.pl.highest_expr_genes(adata, n_top=20)
/Users/jwhittle/opt/anaconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/preprocessing/_normalization.py:170: UserWarning: Received a view of an AnnData. Making a copy. view_to_actual(adata)
normalizing counts per cell
finished (0:00:01)
# Using biomart to identify relevant genes
annot = sc.queries.biomart_annotations(
"hsapiens",
["ensembl_gene_id", "external_gene_name", "start_position", "end_position", "chromosome_name"],
).set_index("external_gene_name")
# Find percentage chrY
chrY_genes = adata.var_names.intersection(annot.index[annot.chromosome_name == "Y"])
adata.obs['percent_chrY'] = np.sum(
adata[:, chrY_genes].X, axis=1) / np.sum(adata.X, axis=1) * 100
# XIST Proportion
adata.obs["XIST-counts"] = adata.X[:,adata.var_names.str.match('XIST')].toarray()
sc.pl.violin(adata, ["XIST-counts", "percent_chrY"], groupby = 'sample',
rotation=45, jitter=0.4)
adata.layers["counts"] = adata.X.copy()
adata.write_h5ad(combined_file)
# Log normalization
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
normalizing counts per cell
finished (0:00:01)
# Get highly variable genes
sc.pp.highly_variable_genes(
adata,
flavor="seurat_v3",
n_top_genes=2000,
layer="counts",
batch_key="sample",
subset=True,
)
If you pass `n_top_genes`, all cutoffs are ignored.
extracting highly variable genes
--> added
'highly_variable', boolean vector (adata.var)
'highly_variable_rank', float vector (adata.var)
'means', float vector (adata.var)
'variances', float vector (adata.var)
'variances_norm', float vector (adata.var)
# PCA
sc.tl.pca(adata, svd_solver='arpack')
sc.pl.pca_variance_ratio(adata, log=True)
computing PCA
on highly variable genes
with n_comps=50
finished (0:00:05)
# Calculate neighbours graph
sc.pp.neighbors(adata, n_pcs = 30, n_neighbors = 20)
# UMAP
sc.tl.umap(adata)
sc.pl.umap(adata, color='sample', title='Zhang 2023: Uncorrected')
computing neighbors
using 'X_pca' with n_pcs = 30
OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:01:27)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:43)
/Users/jwhittle/opt/anaconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored cax = scatter(
sc.pl.umap(adata, color=["CD34","CD14",'IL7R'])
adata_ss = sc.pp.subsample(adata, fraction=0.2, copy=True)
# Make Expression Matrix
count = pd.DataFrame(adata_ss.X.A, columns=adata_ss.var.index, index = adata_ss.obs.index).T
# Get batch ids
batch = adata_ss.obs["sample"]
%%R -i count,batch
batch <- as.factor(batch)
batch.estimate <- kBET(count, batch)
sce.pp.harmony_integrate(adata, 'sample')
2024-10-14 22:06:07,975 - harmonypy - INFO - Computing initial centroids with sklearn.KMeans... 2024-10-14 22:06:23,324 - harmonypy - INFO - sklearn.KMeans initialization complete. 2024-10-14 22:06:23,502 - harmonypy - INFO - Iteration 1 of 10 2024-10-14 22:06:37,751 - harmonypy - INFO - Iteration 2 of 10 2024-10-14 22:06:51,844 - harmonypy - INFO - Iteration 3 of 10 2024-10-14 22:07:05,849 - harmonypy - INFO - Iteration 4 of 10 2024-10-14 22:07:21,499 - harmonypy - INFO - Iteration 5 of 10 2024-10-14 22:07:31,877 - harmonypy - INFO - Converged after 5 iterations
sc.pp.neighbors(adata, use_rep="X_pca_harmony")
sc.tl.umap(adata)
sc.pl.umap(adata, color='sample', title='Zhang 2023: Harmony')
sc.pl.umap(adata, color=["CD34","CD14",'IL7R'])
computing neighbors
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:04)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:39)
/Users/jwhittle/opt/anaconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored cax = scatter(
scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="sample")
vae = scvi.model.SCVI(adata, n_layers=2, n_latent=30, gene_likelihood="nb")
vae.train()
GPU available: True (mps), used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs /Users/jwhittle/opt/anaconda3/envs/scvi-env/lib/python3.9/site-packages/pytorch_lightning/trainer/setup.py:201: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`. rank_zero_warn(
Epoch 106/106: 100%|███████| 106/106 [16:35<00:00, 9.38s/it, loss=707, v_num=1]
`Trainer.fit` stopped: `max_epochs=106` reached.
Epoch 106/106: 100%|███████| 106/106 [16:35<00:00, 9.40s/it, loss=707, v_num=1]
adata.obsm["X_scVI"] = vae.get_latent_representation()
sc.pp.neighbors(adata, use_rep="X_scVI")
sc.tl.umap(adata)
sc.pl.umap(adata, color='sample', title='Zhang 2023: SCVI')
sc.pl.umap(adata, color=["CD34","CD14",'IL7R'])
computing neighbors
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:04)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:36)
/Users/jwhittle/opt/anaconda3/envs/scvi-env/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored cax = scatter(
adata.write_h5ad(corrected_file)
!conda list
# packages in environment at /Users/jwhittle/opt/anaconda3: # # Name Version Build Channel _ipyw_jlab_nb_ext_conf 0.1.0 py39hca03da5_1 absl-py 1.4.0 pypi_0 pypi adjusttext 1.2.0 pypi_0 pypi aiohttp 3.8.4 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi alabaster 0.7.12 pyhd3eb1b0_0 anaconda 2022.10 py39_0 anaconda-client 1.11.0 py39hca03da5_0 anaconda-navigator 2.3.1 py39hca03da5_0 anaconda-project 0.11.1 py39hca03da5_0 anndata 0.8.0 pypi_0 pypi annoy 1.17.1 pypi_0 pypi anyio 3.5.0 py39hca03da5_0 appdirs 1.4.4 pyhd3eb1b0_0 appnope 0.1.2 py39hca03da5_1001 appscript 1.1.2 py39h1a28f6b_0 arboreto 0.1.6 pypi_0 pypi argon2-cffi 21.3.0 pyhd3eb1b0_0 argon2-cffi-bindings 21.2.0 py39h1a28f6b_0 arpack 3.7.0 h58ebc17_2 conda-forge array-api-compat 1.4 pyhd8ed1ab_0 conda-forge arrow 1.2.3 pypi_0 pypi arviz 0.17.1 pypi_0 pypi astroid 2.11.7 py39hca03da5_0 astropy 5.1 py39heec5a64_0 asttokens 2.0.5 pyhd3eb1b0_0 async-timeout 4.0.2 pypi_0 pypi attrs 21.4.0 pyhd3eb1b0_0 automat 20.2.0 py_0 babel 2.9.1 pyhd3eb1b0_0 backcall 0.2.0 pyhd3eb1b0_0 backoff 2.2.1 pypi_0 pypi backports 1.1 pyhd3eb1b0_0 backports.functools_lru_cache 1.6.4 pyhd3eb1b0_0 backports.tempfile 1.0 pyhd3eb1b0_1 backports.weakref 1.0.post1 py_1 bash-kernel 0.9.1 pypi_0 pypi bcrypt 3.2.0 py39h1a28f6b_1 beautifulsoup4 4.11.1 py39hca03da5_0 biopython 1.81 pypi_0 pypi biothings-client 0.2.6 pypi_0 pypi bitarray 2.5.1 py39h1a28f6b_0 bkcharts 0.2 py39hca03da5_1 blas 1.0 openblas bleach 4.1.0 pyhd3eb1b0_0 blessed 1.20.0 pypi_0 pypi blosc 1.21.0 h98b2900_1 bokeh 2.4.3 py39hca03da5_0 boltons 23.0.0 pypi_0 pypi boto3 1.24.28 py39hca03da5_0 botocore 1.27.28 py39hca03da5_0 bottleneck 1.3.5 py39heec5a64_0 brotli 1.0.9 h1a28f6b_7 brotli-bin 1.0.9 h1a28f6b_7 brotlipy 0.7.0 py39h1a28f6b_1002 brunsli 0.1 hc377ac9_1 bzip2 1.0.8 h620ffc9_4 c-ares 1.18.1 h1a28f6b_0 ca-certificates 2022.07.19 hca03da5_0 cached-property 1.5.2 pypi_0 pypi cctools 949.0.1 hc179dcd_25 cctools_osx-arm64 949.0.1 h332cad3_25 celltypist 1.3.0 pypi_0 pypi certifi 2022.9.24 py39hca03da5_0 cffi 1.15.1 py39h22df2f2_0 cfitsio 3.470 h7f6438f_7 chardet 4.0.0 py39hca03da5_1003 charls 2.2.0 hc377ac9_0 charset-normalizer 2.0.4 pyhd3eb1b0_0 chex 0.1.6 pypi_0 pypi click 8.0.4 py39hca03da5_0 cloudpickle 2.0.0 pyhd3eb1b0_0 clyent 1.2.1 pypi_0 pypi cmake 3.26.3 pypi_0 pypi colorama 0.4.5 py39hca03da5_0 colorcet 3.0.0 py39hca03da5_0 conda 22.9.0 py39hca03da5_0 conda-build 3.22.0 py39hca03da5_0 conda-content-trust 0.1.3 py39hca03da5_0 conda-env 2.6.0 hca03da5_1 conda-pack 0.7.1 pyhd8ed1ab_0 conda-forge conda-package-handling 2.2.0 pyh38be061_0 conda-forge conda-package-streaming 0.9.0 pyhd8ed1ab_0 conda-forge conda-repo-cli 1.0.20 py39hca03da5_0 conda-token 0.4.0 pyhd3eb1b0_0 conda-verify 3.4.2 py_1 constantly 15.1.0 pyh2b92418_0 contextlib2 21.6.0 pypi_0 pypi contourpy 1.3.0 pypi_0 pypi croniter 1.4.1 pypi_0 pypi cryptography 37.0.1 py39h834c97f_0 cssselect 1.1.0 pyhd3eb1b0_0 ctxcore 0.2.0 pypi_0 pypi curl 7.84.0 h1a28f6b_0 custom-inherit 2.4.1 pypi_0 pypi 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